---

name: training-and-cli description: "Routes TensorLayer training loops, evaluation helpers, and CLI workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training and CLI

Use this sub-skill for TensorLayer supervised training loops, evaluation helpers, CLI help, and distributed-trainer entry points. This is the route for fit, test, predict, and tl train style tasks.

Typical requests

• Run a tiny TensorLayer training loop.
• Check tl.utils.fit, tl.utils.test, or tl.utils.predict usage.
• Inspect the tensorlayer.cli entry point or build_arg_parser behavior.
• Understand how tl train or the distributed trainer is wired.

Read first

• references/cli-reference.md for the CLI and utility API surface.
• references/workflows.md for a tiny synthetic fit/test/predict loop.
• references/troubleshooting.md for CLI, dataset, and distributed-training failures.

Bundled checks

• scripts/smoke_fit.py runs a small synthetic classification problem through tl.utils.fit, tl.utils.test, and tl.utils.predict.
• Root scripts/check_cli_help.py verifies python -m tensorlayer.cli --help without the empty-CUDA_VISIBLE_DEVICES bug.

Boundaries

Include here:

• tensorlayer.utils
• tensorlayer.cli
• tensorlayer.distributed
• fit/test/predict and evaluation helpers
• training-loop and parser behavior

Exclude or route elsewhere:

• layer/model architecture details -> core-modeling
• preprocessing and TFRecord helpers -> data-and-utilities
• pretrained vision model constructors -> vision-and-apps
• text, seq2seq, and NLP helpers -> text-and-sequence
• reward utilities and RL examples -> reinforcement-learning

Fast path

1. Check whether the task is about a training loop, evaluation helper, or CLI parser.
2. Use synthetic data first; do not depend on MNIST/CIFAR downloads for a smoke.
3. Treat distributed execution as help-only unless the user explicitly requests a Horovod/OpenMPI environment.